diff --git a/.gitignore b/.gitignore index 82f9275..dee2e10 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,7 @@ +*.pth + # Byte-compiled / optimized / DLL files +__pycache__ __pycache__/ *.py[cod] *$py.class diff --git a/README.md b/README.md index 4a663e5..05193a2 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,13 @@ # FM_nodes -A few ComfyUI nodes, starting with WFEN, [Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network](https://github.com/PRIS-CV/WFEN) +A collection of ComfyUI nodes. Including: +1. WFEN Face Restore, [Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network](https://github.com/PRIS-CV/WFEN) + + +## Workflows + +### WFEN + +[workflow_wfen_facecrop.json](workflow/workflow_wfen_facecrop.json) + +![wfen_facecrop](workflow/wfen_facecrop.png) \ No newline at end of file diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..5b4aa13 --- /dev/null +++ b/__init__.py @@ -0,0 +1,14 @@ +from .run import ( + WFEN, +) + +NODE_CLASS_MAPPINGS = { + "WFEN": WFEN +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "WFEN": "WFEN Face Restore" +} + + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] \ No newline at end of file diff --git a/constants.py b/constants.py new file mode 100644 index 0000000..d4bea4f --- /dev/null +++ b/constants.py @@ -0,0 +1 @@ +WFEN_MODEL_PATH = "models/wfen/WFEN.pth" \ No newline at end of file diff --git a/examples/cj.png b/examples/cj.png new file mode 100644 index 0000000..d4e22aa Binary files /dev/null and b/examples/cj.png differ diff --git a/examples/obama.png b/examples/obama.png new file mode 100644 index 0000000..93f769a Binary files /dev/null and b/examples/obama.png differ diff --git a/models/.gitkeep b/models/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..8d887b0 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,2 @@ +torch +einops \ No newline at end of file diff --git a/run.py b/run.py new file mode 100644 index 0000000..1f17c2b --- /dev/null +++ b/run.py @@ -0,0 +1,48 @@ +""" +@author: Fuou Marinas +@title: FM Nodes +@nickname: FM_nodes +@description: A collection of nodes. WFEN Face Super Resolution. +""" + +from pathlib import Path + +import torch + +from .constants import WFEN_MODEL_PATH +from .utils import ensure_size +from .wfen_module.wfen_model import WFENModel + +base_dir = Path(__file__).resolve().parent + + +class WFEN: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "src_img": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("res_img",) + FUNCTION = "todo" + CATEGORY = "FM_nodes" + + def todo( + self, + src_img: torch.Tensor, + ): + print(f"{src_img.shape=}") + source_tensor = ensure_size(src_img.permute(0, 3, 1, 2)) + print(f"{source_tensor.shape=}") + wfening = WFENModel() + wfening.for_load_pretrain_model(f"{base_dir}/{WFEN_MODEL_PATH}") + + out_tensor = wfening.netG.forward(source_tensor) + # out_tensor = ensure_size(out_tensor, size=src_img.shape[1:3]) + print(f"{out_tensor.shape=}") + out_tensor = out_tensor.permute(0, 2, 3, 1) + + return (out_tensor,) diff --git a/test.ipynb b/test.ipynb new file mode 100644 index 0000000..b11a21a --- /dev/null +++ b/test.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from wfen_module.wfen_model import WFENModel\n", + "from PIL import Image\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading pretrained model models/wfen/WFEN.pth\n" + ] + } + ], + "source": [ + "wfening = WFENModel()\n", + "wfening.for_load_pretrain_model(\"models/wfen/WFEN.pth\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "j:\\AI\\ComfyUI_windows_portable\\python_embeded\\Lib\\site-packages\\transformers\\utils\\hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# img = Image.open(\"test.jpg\").convert('RGB')\n", + "# img = Image.open(\"test_2.png\").convert('RGB')\n", + "img = Image.open(\"test_3.png\").convert('RGB')\n", + "img_np = np.array(img)\n", + "img_cv = img_np / 255 * 2 - 1\n", + "img_tensor = torch.from_numpy(img_cv.transpose(2, 0, 1)).unsqueeze(0).to('cuda') \n", + "img_tensor = img_tensor.float()\n", + "out_tensor = wfening.netG.forward(img_tensor)\n", + "\n", + "img_array = out_tensor.squeeze().data.cpu().numpy()\n", + "img_array = img_array.transpose(1, 2, 0)\n", + "new_img_arr = ((img_array + 1) / 2) * 255\n", + "cv_img = new_img_arr.clip(0, 255).astype(np.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[[[-0.8353, -0.8431, -0.8353, ..., -0.7333, -0.7333, -0.7333],\n", + " [-0.8353, -0.8431, -0.8353, ..., -0.7333, -0.7333, -0.7333],\n", + " [-0.8353, -0.8431, -0.8353, ..., -0.7333, -0.7333, -0.7333],\n", + " ...,\n", + " [-0.6235, -0.6157, -0.6235, ..., 0.4902, 0.4980, 0.4902],\n", + " [-0.6157, -0.6157, -0.6157, ..., 0.4824, 0.4902, 0.4902],\n", + " [-0.6157, -0.6157, -0.6235, ..., 0.4824, 0.4980, 0.4902]],\n", + "\n", + " [[-0.8275, -0.8275, -0.8196, ..., -0.6941, -0.6941, -0.6941],\n", + " [-0.8275, -0.8275, -0.8196, ..., -0.6941, -0.6941, -0.6941],\n", + " [-0.8275, -0.8275, -0.8196, ..., -0.6941, -0.6941, -0.6941],\n", + " ...,\n", + " [-0.5373, -0.5294, -0.5373, ..., 0.7569, 0.7725, 0.7647],\n", + " [-0.5373, -0.5294, -0.5373, ..., 0.7569, 0.7647, 0.7647],\n", + " [-0.5373, -0.5294, -0.5373, ..., 0.7569, 0.7647, 0.7647]],\n", + "\n", + " [[-0.9922, -0.9922, -0.9922, ..., -0.8824, -0.8824, -0.8824],\n", + " [-0.9922, -0.9922, -0.9922, ..., -0.8824, -0.8824, -0.8824],\n", + " [-0.9922, -0.9922, -0.9922, ..., -0.8902, -0.8824, -0.8824],\n", + " ...,\n", + " [-0.6627, -0.6549, -0.6627, ..., 1.0000, 1.0000, 1.0000],\n", + " [-0.6627, -0.6627, -0.6627, ..., 1.0000, 1.0000, 1.0000],\n", + " [-0.6627, -0.6627, -0.6627, ..., 1.0000, 1.0000, 1.0000]]]],\n", + " device='cuda:0')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_tensor" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 3, 128, 128])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_tensor.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(cv_img)" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..1e1787b --- /dev/null +++ b/utils.py @@ -0,0 +1,11 @@ +import torch +import torch.nn.functional as F + + +def ensure_size(tensor: torch.Tensor, size: tuple[int, int] = (128, 128)): + if tensor.shape[-2:] == size: + return tensor + else: + return F.interpolate( + tensor, size=size, mode="bilinear", align_corners=False + ) diff --git a/wfen_module/arch_blocks.py b/wfen_module/arch_blocks.py new file mode 100644 index 0000000..6a4da22 --- /dev/null +++ b/wfen_module/arch_blocks.py @@ -0,0 +1,372 @@ +import math +import numbers + +import torch +import torch.nn as nn +from einops import rearrange +from torch.nn import functional as F + + +# --------------------------------------------------------------------------------------------------------------------- +# Layer Norm +def to_3d(x): + return rearrange(x, "b c h w -> b (h w) c") + + +def to_4d(x, h, w): + return rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + + +class BiasFree_LayerNorm(nn.Module): + def __init__(self, normalized_shape): + super(BiasFree_LayerNorm, self).__init__() + if isinstance(normalized_shape, numbers.Integral): + normalized_shape = (normalized_shape,) + normalized_shape = torch.Size(normalized_shape) + + assert len(normalized_shape) == 1 + + self.weight = nn.Parameter(torch.ones(normalized_shape)) + self.normalized_shape = normalized_shape + + def forward(self, x): + sigma = x.var(-1, keepdim=True, unbiased=False) + return x / torch.sqrt(sigma + 1e-5) * self.weight + + +class WithBias_LayerNorm(nn.Module): + def __init__(self, normalized_shape): + super(WithBias_LayerNorm, self).__init__() + if isinstance(normalized_shape, numbers.Integral): + normalized_shape = (normalized_shape,) + normalized_shape = torch.Size(normalized_shape) + + assert len(normalized_shape) == 1 + + self.weight = nn.Parameter(torch.ones(normalized_shape)) + self.bias = nn.Parameter(torch.zeros(normalized_shape)) + self.normalized_shape = normalized_shape + + def forward(self, x): + mu = x.mean(-1, keepdim=True) + sigma = x.var(-1, keepdim=True, unbiased=False) + return (x - mu) / torch.sqrt(sigma + 1e-5) * self.weight + self.bias + + +class LayerNorm(nn.Module): + def __init__(self, dim, LayerNorm_type): + super(LayerNorm, self).__init__() + if LayerNorm_type == "BiasFree": + self.body = BiasFree_LayerNorm(dim) + else: + self.body = WithBias_LayerNorm(dim) + + def forward(self, x): + h, w = x.shape[-2:] + return to_4d(self.body(to_3d(x)), h, w) + + +# --------------------------------------------------------------------------------------------------------------------- + + +# FFN +class FeedForward(nn.Module): + def __init__(self, dim, ffn_expansion_factor, bias, input_resolution=None): + super(FeedForward, self).__init__() + + self.input_resolution = input_resolution + self.dim = dim + self.ffn_expansion_factor = ffn_expansion_factor + + hidden_features = int(dim * ffn_expansion_factor) + self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias) + self.dwconv = nn.Conv2d( + hidden_features * 2, + hidden_features * 2, + kernel_size=3, + stride=1, + padding=1, + groups=hidden_features * 2, + bias=bias, + ) + self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias) + + def forward(self, x): + x = self.project_in(x) + x1, x2 = self.dwconv(x).chunk(2, dim=1) + x = F.gelu(x1) * x2 + x = self.project_out(x) + return x + + +class NormLayer(nn.Module): + """Normalization Layers. + ------------ + # Arguments + - channels: input channels, for batch norm and instance norm. + - input_size: input shape without batch size, for layer norm. + """ + + def __init__(self, channels, normalize_shape=None, norm_type="bn"): + super(NormLayer, self).__init__() + norm_type = norm_type.lower() + if norm_type == "bn": + self.norm = nn.BatchNorm2d(channels) + elif norm_type == "in": + self.norm = nn.InstanceNorm2d(channels, affine=True) + elif norm_type == "gn": + self.norm = nn.GroupNorm(32, channels, affine=True) + elif norm_type == "pixel": + self.norm = lambda x: F.normalize(x, p=2, dim=1) + elif norm_type == "layer": + self.norm = nn.LayerNorm(normalize_shape) + elif norm_type == "none": + self.norm = lambda x: x + else: + assert 1 == 0, "Norm type {} not support.".format(norm_type) + + def forward(self, x): + return self.norm(x) + + +class ReluLayer(nn.Module): + """Relu Layer. + ------------ + # Arguments + - relu type: type of relu layer, candidates are + - ReLU + - LeakyReLU: default relu slope 0.2 + - PRelu + - SELU + - none: direct pass + """ + + def __init__(self, channels, relu_type="relu"): + super(ReluLayer, self).__init__() + relu_type = relu_type.lower() + if relu_type == "relu": + self.func = nn.ReLU(True) + elif relu_type == "leakyrelu": + self.func = nn.LeakyReLU(0.2, inplace=True) + elif relu_type == "prelu": + self.func = nn.PReLU(channels) + elif relu_type == "selu": + self.func = nn.SELU(True) + elif relu_type == "none": + self.func = lambda x: x + else: + assert 1 == 0, "Relu type {} not support.".format(relu_type) + + def forward(self, x): + return self.func(x) + + +class ConvLayer(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size=3, + scale="none", + norm_type="none", + relu_type="none", + use_pad=True, + ): + super(ConvLayer, self).__init__() + self.use_pad = use_pad + + bias = True if norm_type in ["pixel", "none"] else False + stride = 2 if scale == "down" else 1 + + self.scale_func = lambda x: x + if scale == "up": + self.scale_func = lambda x: nn.functional.interpolate( + x, scale_factor=2, mode="nearest" + ) + + self.reflection_pad = nn.ReflectionPad2d(kernel_size // 2) + self.conv2d = nn.Conv2d( + in_channels, out_channels, kernel_size, stride, bias=bias + ) + + self.relu = ReluLayer(out_channels, relu_type) + self.norm = NormLayer(out_channels, norm_type=norm_type) + + def forward(self, x): + out = self.scale_func(x) + if self.use_pad: + out = self.reflection_pad(out) + out = self.conv2d(out) + out = self.norm(out) + out = self.relu(out) + return out + + +class ResidualBlock(nn.Module): + """ + Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html + ------------------ + # Args + - hg_depth: depth of HourGlassBlock. 0: don't use attention map. + - use_pmask: whether use previous mask as HourGlassBlock input. + """ + + def __init__( + self, + c_in, + c_out, + relu_type="prelu", + norm_type="bn", + scale="none", + hg_depth=2, + att_name="spar", + ): + super(ResidualBlock, self).__init__() + self.c_in = c_in + self.c_out = c_out + self.norm_type = norm_type + self.relu_type = relu_type + self.hg_depth = hg_depth + + kwargs = {"norm_type": norm_type, "relu_type": relu_type} + + if scale == "none" and c_in == c_out: + self.shortcut_func = lambda x: x + else: + self.shortcut_func = ConvLayer(c_in, c_out, 3, scale) + + self.preact_func = nn.Sequential( + NormLayer(c_in, norm_type=self.norm_type), + ReluLayer(c_in, self.relu_type), + ) + + if scale == "down": + scales = ["none", "down"] + elif scale == "up": + scales = ["up", "none"] + elif scale == "none": + scales = ["none", "none"] + + self.conv1 = ConvLayer(c_in, c_out, 3, scales[0], **kwargs) + self.conv2 = ConvLayer( + c_out, c_out, 3, scales[1], norm_type=norm_type, relu_type="none" + ) + + if att_name.lower() == "spar": + c_attn = 1 + elif att_name.lower() == "spar3d": + c_attn = c_out + else: + raise Exception("Attention type {} not implemented".format(att_name)) + + self.att_func = HourGlassBlock(self.hg_depth, c_out, c_attn, **kwargs) + + def forward(self, x): + identity = self.shortcut_func(x) + out = self.preact_func(x) + out = self.conv1(out) + out = self.conv2(out) + out = identity + self.att_func(out) + return out + + +class HourGlassBlock(nn.Module): + """Simplified HourGlass block. + Reference: https://github.com/1adrianb/face-alignment + -------------------------- + """ + + def __init__( + self, + depth, + c_in, + c_out, + c_mid=64, + norm_type="bn", + relu_type="prelu", + ): + super(HourGlassBlock, self).__init__() + self.depth = depth + self.c_in = c_in + self.c_mid = c_mid + self.c_out = c_out + self.kwargs = {"norm_type": norm_type, "relu_type": relu_type} + + if self.depth: + self._generate_network(self.depth) + self.out_block = nn.Sequential( + ConvLayer(self.c_mid, self.c_out, norm_type="none", relu_type="none"), + nn.Sigmoid(), + ) + + def _generate_network(self, level): + if level == self.depth: + c1, c2 = self.c_in, self.c_mid + else: + c1, c2 = self.c_mid, self.c_mid + + self.add_module("b1_" + str(level), ConvLayer(c1, c2, **self.kwargs)) + self.add_module( + "b2_" + str(level), ConvLayer(c1, c2, scale="down", **self.kwargs) + ) + if level > 1: + self._generate_network(level - 1) + else: + self.add_module( + "b2_plus_" + str(level), + ConvLayer(self.c_mid, self.c_mid, **self.kwargs), + ) + + self.add_module( + "b3_" + str(level), + ConvLayer(self.c_mid, self.c_mid, scale="up", **self.kwargs), + ) + + def _forward(self, level, in_x): + up1 = self._modules["b1_" + str(level)](in_x) + low1 = self._modules["b2_" + str(level)](in_x) + if level > 1: + low2 = self._forward(level - 1, low1) + else: + low2 = self._modules["b2_plus_" + str(level)](low1) + + up2 = self._modules["b3_" + str(level)](low2) + if up1.shape[2:] != up2.shape[2:]: + up2 = nn.functional.interpolate(up2, up1.shape[2:]) + + return up1 + up2 + + def forward(self, x, pmask=None): + if self.depth == 0: + return x + input_x = x + x = self._forward(self.depth, x) + self.att_map = self.out_block(x) + x = input_x * self.att_map + return x + + +class UpSampler(nn.Sequential): + def __init__(self, scale, n_feats, out_feats): + m = [] + if scale == 8: + kernel_size = 3 + elif scale == 16: + kernel_size = 5 + else: + kernel_size = 1 + + if (scale & (scale - 1)) == 0: # Is scale = 2^n? + for _ in range(int(math.log(scale, 2))): + m.append( + nn.Conv2d( + in_channels=n_feats, + out_channels=4 * out_feats, + kernel_size=kernel_size, + stride=1, + padding=kernel_size // 2, + ) + ) + m.append(nn.PixelShuffle(upscale_factor=2)) + m.append(nn.PReLU()) + super(UpSampler, self).__init__(*m) diff --git a/wfen_module/models.py b/wfen_module/models.py new file mode 100644 index 0000000..c00ba7b --- /dev/null +++ b/wfen_module/models.py @@ -0,0 +1,468 @@ +import torch +import torch.nn.functional as F +from einops import rearrange +from torch import nn + +from .arch_blocks import FeedForward, LayerNorm + + +class GSA(nn.Module): + def __init__(self, channels, num_heads=8, bias=False): + super(GSA, self).__init__() + self.channels = channels + self.num_heads = num_heads + + self.temperature = nn.Parameter(torch.ones(1, 1, 1)) + self.act = nn.ReLU() + + self.qkv = nn.Conv2d(channels, channels * 3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d( + channels * 3, + channels * 3, + kernel_size=3, + stride=1, + padding=1, + groups=channels * 3, + bias=bias, + ) + self.project_out = nn.Conv2d(channels, channels, kernel_size=1, bias=bias) + + def forward(self, x, prev_atns=None): + b, c, h, w = x.shape + if prev_atns is None: + qkv = self.qkv_dwconv(self.qkv(x)) + q, k, v = qkv.chunk(3, dim=1) + q = rearrange(q, "b (head c) h w -> b head c (h w)", head=self.num_heads) + k = rearrange(k, "b (head c) h w -> b head c (h w)", head=self.num_heads) + v = rearrange(v, "b (head c) h w -> b head c (h w)", head=self.num_heads) + + q = torch.nn.functional.normalize(q, dim=-1) + k = torch.nn.functional.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = self.act(attn) + out = attn @ v + y = rearrange( + out, "b head c (h w) -> b (head c) h w", head=self.num_heads, h=h, w=w + ) + y = rearrange( + y, "b (head c) h w -> b (c head) h w", head=self.num_heads, h=h, w=w + ) + y = self.project_out(y) + return y, attn + else: + attn = prev_atns + v = rearrange(x, "b (head c) h w -> b head c (h w)", head=self.num_heads) + out = attn @ v + y = rearrange( + out, "b head c (h w) -> b (head c) h w", head=self.num_heads, h=h, w=w + ) + y = rearrange( + y, "b (head c) h w -> b (c head) h w", head=self.num_heads, h=h, w=w + ) + y = self.project_out(y) + return y + + +class RSA(nn.Module): + def __init__( + self, channels, num_heads, shifts=1, window_sizes=[4, 8, 12], bias=False + ): + super(RSA, self).__init__() + self.channels = channels + self.shifts = shifts + self.window_sizes = window_sizes + + self.temperature = nn.Parameter(torch.ones(1, 1, 1)) + self.act = nn.ReLU() + + self.qkv = nn.Conv2d(channels, channels * 3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d( + channels * 3, + channels * 3, + kernel_size=3, + stride=1, + padding=1, + groups=channels * 3, + bias=bias, + ) + self.project_out = nn.Conv2d(channels, channels, kernel_size=1, bias=bias) + + def forward(self, x, prev_atns=None): + b, c, h, w = x.shape + if prev_atns is None: + wsize = self.window_sizes + x_ = x + if self.shifts > 0: + x_ = torch.roll(x_, shifts=(-wsize // 2, -wsize // 2), dims=(2, 3)) + qkv = self.qkv_dwconv(self.qkv(x_)) + q, k, v = qkv.chunk(3, dim=1) + q = rearrange( + q, "b c (h dh) (w dw) -> b (h w) (dh dw) c", dh=wsize, dw=wsize + ) + k = rearrange( + k, "b c (h dh) (w dw) -> b (h w) (dh dw) c", dh=wsize, dw=wsize + ) + v = rearrange( + v, "b c (h dh) (w dw) -> b (h w) (dh dw) c", dh=wsize, dw=wsize + ) + + q = torch.nn.functional.normalize(q, dim=-1) + k = torch.nn.functional.normalize(k, dim=-1) + + attn = ( + q.transpose(-2, -1) @ k + ) * self.temperature # b (h w) (dh dw) (dh dw) + attn = self.act(attn) + out = v @ attn + out = rearrange( + out, + "b (h w) (dh dw) c-> b (c) (h dh) (w dw)", + h=h // wsize, + w=w // wsize, + dh=wsize, + dw=wsize, + ) + if self.shifts > 0: + out = torch.roll(out, shifts=(wsize // 2, wsize // 2), dims=(2, 3)) + y = self.project_out(out) + return y, attn + else: + wsize = self.window_sizes + if self.shifts > 0: + x = torch.roll(x, shifts=(-wsize // 2, -wsize // 2), dims=(2, 3)) + atn = prev_atns + v = rearrange( + x, "b (c) (h dh) (w dw) -> b (h w) (dh dw) c", dh=wsize, dw=wsize + ) + y_ = v @ atn + y_ = rearrange( + y_, + "b (h w) (dh dw) c-> b (c) (h dh) (w dw)", + h=h // wsize, + w=w // wsize, + dh=wsize, + dw=wsize, + ) + if self.shifts > 0: + y_ = torch.roll(y_, shifts=(wsize // 2, wsize // 2), dims=(2, 3)) + y = self.project_out(y_) + return y + + +class FDT(nn.Module): + def __init__( + self, + inp_channels, + window_sizes, + shifts, + num_heads, + shared_depth=1, + ffn_expansion_factor=2.66, + ): + super(FDT, self).__init__() + self.shared_depth = shared_depth + + modules_ffd = {} + modules_att = {} + modules_norm = {} + for i in range(shared_depth): + modules_ffd["ffd{}".format(i)] = FeedForward( + inp_channels, ffn_expansion_factor, bias=False + ) + modules_att["att_{}".format(i)] = RSA( + channels=inp_channels, + num_heads=num_heads, + shifts=shifts, + window_sizes=window_sizes, + ) + modules_norm["norm_{}".format(i)] = LayerNorm(inp_channels, "WithBias") + modules_norm["norm_{}".format(i + 2)] = LayerNorm(inp_channels, "WithBias") + self.modules_ffd = nn.ModuleDict(modules_ffd) + self.modules_att = nn.ModuleDict(modules_att) + self.modules_norm = nn.ModuleDict(modules_norm) + + modulec_ffd = {} + modulec_att = {} + modulec_norm = {} + for i in range(shared_depth): + modulec_ffd["ffd{}".format(i)] = FeedForward( + inp_channels, ffn_expansion_factor, bias=False + ) + modulec_att["att_{}".format(i)] = GSA( + channels=inp_channels, num_heads=num_heads + ) + modulec_norm["norm_{}".format(i)] = LayerNorm(inp_channels, "WithBias") + modulec_norm["norm_{}".format(i + 2)] = LayerNorm(inp_channels, "WithBias") + self.modulec_ffd = nn.ModuleDict(modulec_ffd) + self.modulec_att = nn.ModuleDict(modulec_att) + self.modulec_norm = nn.ModuleDict(modulec_norm) + + def forward(self, x): + atn = None + B, C, H, W = x.size() + for i in range(self.shared_depth): + if i == 0: ## only calculate attention for the 1-st module + x_, atn = self.modules_att["att_{}".format(i)]( + self.modules_norm["norm_{}".format(i)](x), None + ) + x = ( + self.modules_ffd["ffd{}".format(i)]( + self.modules_norm["norm_{}".format(i + 2)](x_ + x) + ) + + x_ + ) + else: + x_ = self.modules_att["att_{}".format(i)]( + self.modules_norm["norm_{}".format(i)](x), atn + ) + x = ( + self.modules_ffd["ffd{}".format(i)]( + self.modules_norm["norm_{}".format(i + 2)](x_ + x) + ) + + x_ + ) + + for i in range(self.shared_depth): + if i == 0: ## only calculate attention for the 1-st module + x_, atn = self.modulec_att["att_{}".format(i)]( + self.modulec_norm["norm_{}".format(i)](x), None + ) + x = ( + self.modulec_ffd["ffd{}".format(i)]( + self.modulec_norm["norm_{}".format(i + 2)](x_ + x) + ) + + x_ + ) + else: + x = self.modulec_att["att_{}".format(i)]( + self.modulec_norm["norm_{}".format(i)](x), atn + ) + x = ( + self.modulec_ffd["ffd{}".format(i)]( + self.modulec_norm["norm_{}".format(i + 2)](x_ + x) + ) + + x_ + ) + + return x + + +class HaarWavelet(nn.Module): + def __init__(self, in_channels, grad=False): + super(HaarWavelet, self).__init__() + self.in_channels = in_channels + + self.haar_weights = torch.ones(4, 1, 2, 2) + # h + self.haar_weights[1, 0, 0, 1] = -1 + self.haar_weights[1, 0, 1, 1] = -1 + # v + self.haar_weights[2, 0, 1, 0] = -1 + self.haar_weights[2, 0, 1, 1] = -1 + # d + self.haar_weights[3, 0, 1, 0] = -1 + self.haar_weights[3, 0, 0, 1] = -1 + + self.haar_weights = torch.cat([self.haar_weights] * self.in_channels, 0) + self.haar_weights = nn.Parameter(self.haar_weights) + self.haar_weights.requires_grad = grad + + def forward(self, x, rev=False): + if not rev: + out = ( + F.conv2d( + x, self.haar_weights, bias=None, stride=2, groups=self.in_channels + ) + / 4.0 + ) + out = out.reshape( + [x.shape[0], self.in_channels, 4, x.shape[2] // 2, x.shape[3] // 2] + ) + out = torch.transpose(out, 1, 2) + out = out.reshape( + [x.shape[0], self.in_channels * 4, x.shape[2] // 2, x.shape[3] // 2] + ) + return out + else: + out = x.reshape([x.shape[0], 4, self.in_channels, x.shape[2], x.shape[3]]) + out = torch.transpose(out, 1, 2) + out = out.reshape( + [x.shape[0], self.in_channels * 4, x.shape[2], x.shape[3]] + ) + return F.conv_transpose2d( + out, self.haar_weights, bias=None, stride=2, groups=self.in_channels + ) + + +class WFU(nn.Module): + def __init__(self, dim_big, dim_small): + super(WFU, self).__init__() + self.dim = dim_big + self.HaarWavelet = HaarWavelet(dim_big, grad=False) + self.InverseHaarWavelet = HaarWavelet(dim_big, grad=False) + self.RB = nn.Sequential( + nn.Conv2d(dim_big, dim_big, kernel_size=3, padding=1), + nn.ReLU(), + nn.Conv2d(dim_big, dim_big, kernel_size=3, padding=1), + ) + + self.channel_tranformation = nn.Sequential( + nn.Conv2d( + dim_big + dim_small, dim_big + dim_small // 1, kernel_size=1, padding=0 + ), + nn.ReLU(), + nn.Conv2d(dim_big + dim_small // 1, dim_big * 3, kernel_size=1, padding=0), + ) + + def forward(self, x_big, x_small): + haar = self.HaarWavelet(x_big, rev=False) + a = haar.narrow(1, 0, self.dim) + h = haar.narrow(1, self.dim, self.dim) + v = haar.narrow(1, self.dim * 2, self.dim) + d = haar.narrow(1, self.dim * 3, self.dim) + + hvd = self.RB(h + v + d) + a_ = self.channel_tranformation(torch.cat([x_small, a], dim=1)) + out = self.InverseHaarWavelet(torch.cat([hvd, a_], dim=1), rev=True) + + return out + + +class WFD(nn.Module): + def __init__(self, dim_in, dim, need=False): + super(WFD, self).__init__() + self.need = need + if need: + self.first_conv = nn.Conv2d(dim_in, dim, kernel_size=1, padding=0) + self.HaarWavelet = HaarWavelet(dim, grad=False) + self.dim = dim + else: + self.HaarWavelet = HaarWavelet(dim_in, grad=False) + self.dim = dim_in + + def forward(self, x): + if self.need: + x = self.first_conv(x) + + haar = self.HaarWavelet(x, rev=False) + a = haar.narrow(1, 0, self.dim) + h = haar.narrow(1, self.dim, self.dim) + v = haar.narrow(1, self.dim * 2, self.dim) + d = haar.narrow(1, self.dim * 3, self.dim) + + return a, h + v + d + + +class WFEN(nn.Module): + def __init__( + self, + inchannel=3, + min_ch=40, + res_depth=6, + ): + super(WFEN, self).__init__() + self.first_conv = nn.Conv2d(inchannel, min_ch, kernel_size=3, padding=1) + + self.HaarDownsample1 = WFD(min_ch, min_ch * 2, True) + self.HaarDownsample2 = WFD(min_ch * 2, min_ch * 2 * 2, True) + self.HaarDownsample3 = WFD(min_ch * 2 * 2, min_ch * 2 * 2, True) + + self.TransformerDown1 = nn.Sequential( + FDT(inp_channels=min_ch, window_sizes=8, shifts=0, num_heads=4), + FDT(inp_channels=min_ch, window_sizes=8, shifts=0, num_heads=4), + ) + self.TransformerDown2 = nn.Sequential( + FDT(inp_channels=min_ch * 2, window_sizes=4, shifts=1, num_heads=4), + ) + self.TransformerDown3 = nn.Sequential( + FDT(inp_channels=min_ch * 2 * 2, window_sizes=2, shifts=0, num_heads=8), + ) + + self.RB1 = nn.Sequential( + nn.Conv2d(min_ch * 2, min_ch * 2, kernel_size=1, padding=0, groups=1), + nn.ReLU(), + nn.Conv2d(min_ch * 2, min_ch * 2, kernel_size=1, padding=0, groups=1), + ) + self.RB2 = nn.Sequential( + nn.Conv2d( + min_ch * 2 * 2, min_ch * 2 * 2, kernel_size=1, padding=0, groups=1 + ), + nn.ReLU(), + nn.Conv2d( + min_ch * 2 * 2, min_ch * 2 * 2, kernel_size=1, padding=0, groups=1 + ), + ) + self.RB3 = nn.Sequential( + nn.Conv2d( + min_ch * 2 * 2, min_ch * 2 * 2, kernel_size=1, padding=0, groups=1 + ), + nn.ReLU(), + nn.Conv2d( + min_ch * 2 * 2, min_ch * 2 * 2, kernel_size=1, padding=0, groups=1 + ), + ) + + self.Transformer0 = FDT( + inp_channels=min_ch * 2 * 2, window_sizes=1, shifts=0, num_heads=8 + ) + Transformer = [] + for i in range(res_depth - 1): + Transformer.append( + FDT(inp_channels=min_ch * 2 * 2, window_sizes=1, shifts=1, num_heads=8), + ) if i % 2 == 0 else Transformer.append( + FDT(inp_channels=min_ch * 2 * 2, window_sizes=1, shifts=0, num_heads=8) + ) + self.Transformer = nn.Sequential(*Transformer) + + self.TransformerUp1 = nn.Sequential( + FDT(inp_channels=min_ch * 2 * 2, window_sizes=8, shifts=1, num_heads=8), + ) + self.TransformerUp2 = nn.Sequential( + FDT(inp_channels=min_ch * 2, window_sizes=4, shifts=0, num_heads=4), + ) + self.TransformerUp3 = nn.Sequential( + FDT(inp_channels=min_ch, window_sizes=2, shifts=1, num_heads=4), + FDT(inp_channels=min_ch, window_sizes=2, shifts=0, num_heads=4), + ) + + self.HaarFeatureFusion1 = WFU(min_ch * 2 * 2, min_ch * 2 * 2) + self.HaarFeatureFusion2 = WFU(min_ch * 2, min_ch * 2 * 2) + self.HaarFeatureFusion3 = WFU(min_ch, min_ch * 2) + + self.out_conv = nn.Conv2d(min_ch, inchannel, kernel_size=3, padding=1) + + def forward(self, input_img): + x_first = self.first_conv(input_img) + + ############ encoder ############ + x1 = self.TransformerDown1(x_first) # hw:128 c:40 + x1_a, x1_hvd = self.HaarDownsample1(x1) # all hw:64 c:80 + + x2 = self.TransformerDown2(x1_a) # hw:64 c:80 + x2 = x2 + self.RB1(x1_hvd) # hw:64 c:160 + x2_a, x2_hvd = self.HaarDownsample2(x2) # all hw:40 c:160 + + x3 = self.TransformerDown3(x2_a) # hw:40 c:160 + x3 = x3 + self.RB2(x2_hvd) + x3_a, x3_hvd = self.HaarDownsample3(x3) # all hw:16 c:160 + ############ encoder ############ + + x_trans0 = self.Transformer0(x3_a) # hw:16 c:160 + x_trans = self.Transformer(x_trans0) # hw:16 c:160 + x_trans = x_trans + self.RB3(x3_hvd) # hw:16 c:160 + + ############ decoder ############ + x_up1 = self.HaarFeatureFusion1(x3, x_trans) # hw:40 c:160 + x_1 = self.TransformerUp1(x_up1) # hw:40 c:160 + + x_up2 = self.HaarFeatureFusion2(x2, x_1) # hw:64 c:80 + x_2 = self.TransformerUp2(x_up2) # hw:64 c:80 + + x_up3 = self.HaarFeatureFusion3(x1, x_2) # hw:128 c:40 + x_3 = self.TransformerUp3(x_up3) # hw:32 c:40 + ############ decoder ############ + + out_img = self.out_conv(x_3 + x_first) + + return out_img diff --git a/wfen_module/wfen_model.py b/wfen_module/wfen_model.py new file mode 100644 index 0000000..5e7cbe2 --- /dev/null +++ b/wfen_module/wfen_model.py @@ -0,0 +1,32 @@ +import torch +import torch.nn.utils as tutils +from torch import nn + +from .models import WFEN + + +def apply_norm(net: nn.Module, weight_norm_type: str): + for m in net.modules(): + if isinstance(m, nn.Conv2d): + if weight_norm_type.lower() == "spectral_norm": + tutils.spectral_norm(m) + elif weight_norm_type.lower() == "weight_norm": + tutils.weight_norm(m) + else: + pass + + +class WFENModel: + def __init__(self) -> None: + self.device = "cuda" if torch.cuda.is_available() else "cpu" + self.netG = WFEN() + self.netG.to(self.device) + self.netG = nn.DataParallel(self.netG) + + def for_load_pretrain_model(self, path): + print("Loading pretrained model", path) + weight = torch.load(path) + self.netG.module.load_state_dict(weight) + + def forward(self, img_LR): + return self.netG(img_LR) diff --git a/workflow/wfen_facecrop.png b/workflow/wfen_facecrop.png new file mode 100644 index 0000000..f5fc30a Binary files /dev/null and b/workflow/wfen_facecrop.png differ diff --git a/workflow/workflow_wfen_facecrop.json b/workflow/workflow_wfen_facecrop.json new file mode 100644 index 0000000..17b77cd --- /dev/null +++ b/workflow/workflow_wfen_facecrop.json @@ -0,0 +1 @@ +{"last_node_id": 13, "last_link_id": 24, "nodes": [{"id": 10, "type": "PreviewImage", "pos": [628, 362], "size": [210, 246], "flags": {}, "order": 7, "mode": 0, "inputs": [{"name": "images", "type": "IMAGE", "link": 19}], "properties": {"Node name for S&R": "PreviewImage"}}, {"id": 1, "type": "Image Crop Face", "pos": [599, 211], "size": {"0": 315, "1": 102}, "flags": {}, "order": 1, "mode": 0, "inputs": [{"name": "image", "type": "IMAGE", "link": 1, "slot_index": 0}], "outputs": [{"name": "IMAGE", "type": "IMAGE", "links": [2, 21], "shape": 3, "slot_index": 0}, {"name": "CROP_DATA", "type": "CROP_DATA", "links": [13], 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